Data Analytics professional (B.Tech CSE – Data Science, 2026) with hands-on experience in SQL, Python, Power BI, and Tableau across analytics and business intelligence roles. Proven ability to collect, process, and analy…
Data Analytics professional (B.Tech CSE – Data Science, 2026) with hands-on experience in SQL, Python, Power BI, and Tableau across analytics and business intelligence roles. Proven ability to collect, process, and analyze large datasets, design decision-support dashboards, and present actionable insights to stakeholders. Actively targeting Data/Business Analyst roles with a long-term focus on financial analytics at top-tier investment banks.
Developed Python-based predictive models (Bayesian Inference, Logistic Regression) to optimize healthcare resource allocation, improving donor identification efficiency by 2.3% over automated baselines.
Designed and deployed interactive Power BI dashboards.
Performed end-to-end data analysis.
Presented comprehensive data stories to executive stakeholders, enabling data-driven operational decisions.
Developed Python-based predictive models (Bayesian Inference, Logistic Regression) to optimize healthcare resource allocation, improving donor identification efficiency by 2.3% over automated baselines.Designed and deployed interactive Power BI dashboards; performed end-to-end data analysis and presented comprehensive data stories to executive stakeholders, enabling data-driven operational decisions.
Built an ML web application (Flask + Random Forest) to predict manufacturing productivity scores.
Managed the full data pipeline including cleaning, encoding, and feature selection.
Developed dashboards to translate raw production data into actionable efficiency metrics, supporting resource planning and process optimization.
Built an ML web application (Flask + Random Forest) to predict manufacturing productivity scores and managed the full data pipeline including cleaning, encoding, and feature selection.Developed dashboards to translate raw production data into actionable efficiency metrics, supporting resource planning and process optimization.
Drove a 20% increase in user growth and 15% reduction in time-to-market by applying user behavior analysis, workflow automation, Agile methodology, and data-informed product strategy.
Established a KPI framework and data-driven process improvements that reduced recruiter screening time by 60%, demonstrating strong business acumen and metrics reporting capability.
Drove a 20% increase in user growth and 15% reduction in time-to-market by applying user behavior analysis, workflow automation, Agile methodology, and data-informed product strategy.Established a KPI framework and data-driven process improvements that reduced recruiter screening time by 60%, demonstrating strong business acumen and metrics reporting capability.
Built Python pricing models for natural gas storage contracts and credit risk—applying time-series forecasting, logistic regression, and ensemble ML to estimate commodity prices and borrower probability of default (PD) with recovery-rate assumptions.Engineered an optimal FICO score quantization model using dynamic programming (log-likelihood maximization) to categorize 10,000+ mortgage borrowers into monotonic risk-rated buckets, reducing default prediction error across a simulated loan portfolio.
• Engineered an autonomous multi-agent system automating financial due diligence via parallel data ingestion pipelines, reducing preliminary research latency by 50% performing financial analysis.• Integrated real-time market data (yFinance) with sentiment analysis to generate institutional-grade Buy/Hold/Sell investment memos; implemented Pydantic validation ensuring 100% data integrity for valuation metrics (P/E, Market Cap) across international equity markets.
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